Our model links to research on conditional generative modeling [1]}, [2]}, [3]}, including semantic image synthesis and text-to-image generation. The seminal Pix2Pix [4]} and SPADE [5]} works respectively adopt either a U-Net [6]} or spatially-adaptive normalization [7]} to map a given input layout into an output high-resolution image. However, beyond their notable weakness of being conditional and thus unable to create scenes from scratch, their reliance on both static class embeddings for feature modulation, along with perceptual or feature-matching reconstruction losses, render their sample variation rather low [8]}, [9]}. In our work, we mitigate this issue by proposing new purely generative structural loss functions: Semantic Matching and Segment Fidelity, which maintain the one-to-many relation that holds between layouts and images, and consequently, as demonstrated in section REF , significantly enhance the output diversity.
